MIT EECS — Angle Undergraduate Research and Innovation Scholar
Pattern Discovery from Physiological Time Series for Critical Care Patient Monitoring
Patients in intensive care units require constant monitoring in their vital signs. In this project, we will analyze data from physiological time series by using machine learning algorithms to predict patient mortality and the likelihood of the patient developing sepsis. We will model physiological time series using machine learning techniques to discover “clusters” of time series segments with similar trajectories and transient dynamics. Our goal is to identify prototypical temporal patterns from vital sign time series that we can use to generate early warning signs to alert doctors of patients with worsening conditions. By determining the probabilities of adverse events in patients, we can help doctors prioritize patient care.
I have machine learning experience from class that will help me get started, but I know that through my research I’ll learn more than I ever could in the classroom. Having hands on work experience is very valuable. This particular SuperUROP excites me because data is powerful. The MIMIC II data will allow me to make a positive impact in the healthcare sector through machine learning analysis.